Early postoperative refractive outcomes of pediatric intraocular lens implantation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To evaluate the refractive outcome using 5 intraocular lens (IOL) calculation formulas to determine which best predicts refraction after pediatric cataract surgery. SETTING: The Hospital for Sick Children, Toronto, Ontario, Canada. METHODS: This study comprised a review of the charts of 158 consecutive patients aged 2 to 17 years old who were operated on by 1 of 2 staff surgeons between May 1992 and April 2000. The surgeons performed a total of 206 cataract extractions with primary or secondary IOL implantation. The measured outcome was the actual refraction 2 to 6 months postoperatively versus the target refraction. Two regression formulas (SRK, SRK II) and 3 theoretical formulas (Holladay 1, Hoffer Q, SRK/T) were used to predict refractive outcome based on preoperative axial length, corneal curvature, IOL power, and the IOL A-constant provided by the manufacturer. RESULTS: Forty-nine patients (59 IOL implantations) with available data 2 to 6 months after surgery were studied. Also analyzed were data from a subset of 31 patients (34 IOL implantations) with available data 2 to 3 months after surgery. There was poor to moderate agreement between the predicted and actual postoperative refractions using the SRK formula (intraclass correlation coefficient [ICC] = 0.50/0.04 [2- to 3-month follow-up/2- to 6-month follow-up]) and good or fair agreement using the other formulas (ICC from 0.60/0.24 for SRK II to 0.67/0.37 for Hoffer Q). The mean difference between the predicted and actual postoperative refractions with all formulas ranged from 1.06 to 1.22 diopters (D)/1.35 to 1.79 D (median 0.81 to 0.99 D/0.94 to 1.40 D; range 3.03 to 5.57 D/6.75 to 9.21 D). Using Holladay 1 and SRK, 9% to 18%/23% to 39% eyes were more than +/-2.00 D off the target outcome refraction. CONCLUSIONS: All 5 IOL power calculation formulas were unsatisfactory in achieving the target refraction. This finding may have implications for predicting long-term outcomes, interpreting previous reports of refractive outcomes, and obtaining preoperative informed consent in a clinical setting.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it